Examining Potential Socio-economic Factors that Affect Machine Learning Research in the AEC Industry

Machine learning (ML) has increasingly dominated discussions about the shape of mankind's future, permeating almost all facets of our digital, and even physical, world. Yet, contrary to the relentless march of almost all other industries, the architecture, engineering and construction (AEC) industry have lagged behind in the uptake of ML for its own challenges. Through a systematic review of ML projects from a leading global engineering firm, this paper investigates social, political, economic, and cultural (SPEC) factors that have helped or hindered ML's uptake. Further, the paper discusses how ML is perceived at various points in the economic hierarchy, how effective forms of communication is vital in a highly-specialized workforce, and how ML's unexpected effectiveness have forced policy makers to reassess data governance and privacy; all the while considering what this means for the adoption of ML in the AEC industry. This investigation, its methodology, background research, systematic review, and its conclusion are presented.